基于混合遗传算法的多箱型三维装箱问题研究

目的 提高电商仓储领域打包环节包装箱的利用率。方法 针对电商仓储领域多箱型多种物品类型的三维装箱问题,建立混合整数规划的数学模型,设计基于启发式经验规则和多种算子组合的装箱过程模块算法。分别从装箱顺序和带有改进型算子这两方面设计多箱型三维装箱问题混合遗传算法,对装箱方案进行优化。结果 经实验证明,在装箱顺序优化环节PSO–HGA算法系列中,PSO–HGA–S1算法最优。在带有改进算子的混合遗传算法中,IPO–HGA–S1算法最优。结论 文中设计的混合遗传算法能很好地提高电商仓储领域打包环节包装箱的利用率。     

物流车辆路径问题的混合快速蚂蚁算法

通过分析快速蚂蚁算法的原理和易陷入局部最优的缺点,提出了将贪婪算法和快速蚂蚁算法相结合的混合算法求解物流车辆路径问题.混合算法在最优值未改进次数超过限定次数时,自动调用贪婪算法来寻找一个局部最优解,并调整相应路径上信息素的量.为保证解的多样性,对贪婪算法本身使用随机选择第一个客户的方法进行了调整.用计算实例比较并分析了快速蚂蚁算法、混合算法及其他算法应用到车辆路径问题上的结果,说明了贪婪算法使混合算法跳出局部最优的过程以及混合算法的不足之处.     

基于互信息和混合算法的印刷图像配准

目的为了提高印刷图像配准的精度,提出一种基于混合搜索算法的图像配准方法。方法首先求取图像的归一化互信息,然后利用GA算法(遗传算法)进行全局搜索,得出粗配准参数;最后,利用Powell算法进行局部寻优,得出精配准参数。结果混合算法的配准结果与只用单一Powell搜索算法或只用单一GA搜索算法相比,在各个几何变换方向上得到了更小的配准误差。结论与GA算法和Powell算法相比,文中建议的混合算法配准精确度更高、速度更快。     

背包问题的混合粒子群优化算法

经典的粒子群是一个有效的寻找连续函数极值的方法，结合遗传算法的思想提出的混合粒子群算法来解决背包问题，经过比较测试，6种混合粒子群算法的效果都比较好，特别交叉策略A和变异策略C的混合粒子群算法是最好的且简单有效的算法，并成功地运用在投资问题中。对于目前还没有好的解法的组合优化问题，很容易地修改此算法就可解决     

A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs

Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm–simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. The successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region. Copyright © 2004 John Wiley & Sons, Ltd.     

基于改进混合遗传细菌觅食算法的贝叶斯结构学习算法

针对利用启发式学习算法学习贝叶斯网络时容易陷入局部最优和寻优效率低的问题,提出一种改进的混合遗传细菌觅食优化算法的贝叶斯网络结构学习算法。该算法首先通过遗传算法求得较优种群并作为细菌觅食算法的初始种群;然后利用交叉和变异策略改进细菌觅食算法的复制行为,增加种群多样性,扩大搜索空间;最后通过改进细菌觅食算法的迁移行为的初始化操作更新种群,防止精英个体的丢失。通过种群的迭代搜索最终获得最优的贝叶斯网络结构。实验仿真结果表明,与其他算法相比,该算法的收敛精度和效率有所提升。     

混合花朵授粉算法在作业车间调度中的应用

对最大完工时间最短的作业车间调度问题进行了研究,总结了当前求解作业车间调度问题的研究现状,提出一种花朵授粉算法与遗传算法的混合算法。混合算法以花朵授粉算法为基础,重新定义其全局搜索和局部搜索迭代公式,在同化操作过程中融入遗传算法的选择、优先交叉和变异操作,进一步增强算法的勘探能力。通过26个经典的基准算例仿真实验,并与近5年的其他算法比较,结果表明所提算法在求解作业车间调度问题具有一定优势。     

改进遗传-狼群对节点序寻优的贝叶斯网络结构算法

贝叶斯网络是数据挖掘领域的一种重要方法。针对贝叶斯网络结构学习算法寻优效率低和易陷入局部最优的问题，提出一种基于改进的混合遗传-狼群对节点序寻优的贝叶斯网络结构学习算法。该算法首先利用深度优先搜索对最大支撑树的节点进行拓扑排序；然后利用动态变异及最优交叉算子构建适用于节点序寻优的改进捕食行为，引入动态参数因子来增强算法局部寻优能力；最后与K2算法结合得到最优的贝叶斯网络结构。用3种不同大小的标准网络数据集中进行实验，结果表明，该算法收敛到较优值，寻优效率高于其它同类优化算法。     

A smart repair embedded memetic algorithm for 2D shape matching problems

Shape representation plays a major role in any shape optimization exercise. The ability to identify a shape with good performance is dependent on both the flexibility of the shape representation scheme and the efficiency of the optimization algorithm. In this article, a memetic algorithm is presented for 2D shape matching problems. The shape is represented using B-splines, in which the control points representing the shape are repaired and subsequently evolved within the optimization framework. The underlying memetic algorithm is a multi-feature hybrid that combines the strength of a real coded genetic algorithm, differential evolution and a local search. The efficiency of the proposed algorithm is illustrated using three test problems, wherein the shapes were identified using a mere 5000 function evaluations. Extension of the approach to deal with problems of unknown shape complexity is also presented in the article.     

复杂非线性函数最优化问题的一种实用智能算法

对浮点编码遗传算法加以改进,并与DFP变尺度算法相结合,经加速循环,构建新型混合加速遗传算法(以下简称NHAGA);协同求解具有变量边界约束的非凸、高度非线性的复杂函数最优化问题。算例测试表明,该法兼顾了改进浮点编码遗传算法全局搜索能力和DFP算法快速局部搜索能力的优点,成功搜索全局最优点的概率较高,是一种求解非凸、高度非线性全局优化问题的有效智能算法。     

混合粒子群算法在混流装配线优化调度中的应用

应用粒子群算法求解混流装配线的优化调度问题,给出粒子的构造方法,并针对算法中存在过早收敛的问题,提出了一种与局部优化和粒子微变异方法相结合的混合粒子群算法.给出了一个实例,实例应用粒子群算法和混合粒子群算法分别进行求解,与其他一些方法比较表明,混合粒子群算法可以有效、快速地求得混流装配线优化调度问题的解.     

Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

Finding a suitable solution to an optimization problem designed in science is a major challenge. Therefore, these must be addressed utilizing proper approaches. Based on a random search space, optimization algorithms can find acceptable solutions to problems. Archery Algorithm (AA) is a new stochastic approach for addressing optimization problems that is discussed in this study. The fundamental idea of developing the suggested AA is to imitate the archer's shooting behavior toward the target panel. The proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the archer. The AA is mathematically described, and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective functions. Furthermore, the proposed algorithm's performance is compared vs. eight approaches, including teaching-learning based optimization, marine predators algorithm, genetic algorithm, grey wolf optimization, particle swarm optimization, whale optimization algorithm, gravitational search algorithm, and tunicate swarm algorithm. According to the simulation findings, the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios, and it can give adequate quasi-optimal solutions to these problems. The analysis and comparison of competing algorithms’ performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.     

An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization

This article presents an effective hybrid cuckoo search and genetic algorithm (HCSGA) for solving engineering design optimization problems involving problem-specific constraints and mixed variables such as integer, discrete and continuous variables. The proposed algorithm, HCSGA, is first applied to 13 standard benchmark constrained optimization functions and subsequently used to solve three well-known design problems reported in the literature. The numerical results obtained by HCSGA show competitive performance with respect to recent algorithms for constrained design optimization problems.     

Fault diagnostic method of power transformers based on hybrid genetic algorithm evolving wavelet neural network

The main drawbacks of a back propagation algorithm of wavelet neural network (WNN) commonly used in fault diagnosis of power transformers are that the optimal procedure is easily stacked into the local minima and cases that strictly demand initial value. A fault diagnostic method is presented based on a real-encoded hybrid genetic algorithm evolving a WNN, which can be used to optimise the structure and the parameters of WNN instead of humans in the same training process. Through the process, compromise is satisfactorily made among network complexity, convergence and generalisation ability. A number of examples show that the method proposed has good classifying capability for single- and multiple-fault samples of power transformers as well as high fault diagnostic accuracy.     

基于混合遗传算法的多航空集装器装箱优化设计

目的 为了解决当前航空业因航空集装器上货物的组装编排均由人工完成,尚无任何软件系统可以实现自动计算,造成航空货运经济效益和时效性低下的问题,开展航空集装器装箱算法研究.方法 充分利用精英选择策略的精英遗传算法和轮盘赌的简单遗传算法相结合,研究多航空集装器的装箱最优问题.结果 以某航空公司的某国际航线选取了20 d的历史数据来进行实验,计算得出,国际航线平均舱位利用率提升了5％.结论 对多航空集装器装箱进行了装箱模型构建和算法优化,提升了航空货运舱容利用率,装载得到了有效地优化,实现了装箱最优.     

求解约束优化问题的改进布谷鸟搜索算法

为了提高约束优化问题的求解精度和收敛速度,提出求解约束优化问题的改进布谷鸟搜索算法。首先分析了基本布谷鸟搜索算法全局搜索和局部搜索过程中的不足,对其中全局搜索和局部搜索迭代公式进行重新定义,然后以一定概率在最优解附近进行搜索。对12个标准约束优化问题和4个工程约束优化问题进行测试并与多种算法进行对比,实验结果和统计分析表明所提算法在求解约束优化问题上具有较强的优越性。     

Dark Forest Algorithm: A Novel Metaheuristic Algorithm for Global Optimization Problems

Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.     

面向Job-Shop调度的改进免疫算法研究

由于模拟生物免疫系统功能的免疫算法具有解决复杂工程问题的潜力,同时也存在容易陷入局部最优平衡态和进化后期搜索停滞不前的缺陷,在深入分析生物免疫系统机理之后,将生物免疫原理和生物遗传理论集成到免疫算法中,提出了双倍体免疫算法.这种算法采用疫苗、双倍体等多种生物机制,不仅防止了早熟,而且加快了收敛速度.最后通过典型的Job-Shop调度Benchmark问题LA21的求解证实了此算法的有效性和可行性.     

基于MATLAB的一种混合遗传算法的研究

在简要分析简单遗传算法的基础上,介绍了一种改进的混合遗传算法.使用MATLAB语言编制了GA及其改进算法的实现程序,改进算法可以大幅度提高GA用于求解复杂问题的鲁棒性.多峰值函数优化结果表明,该算法能更有效地达到全局最优解.     

基于启发式算法的单航空集装器的装箱优化设计

目的 为了解决当前航空业因航空集装器上货物的组装编排均由人工完成,尚无任何软件系统可以实现自动计算,造成航空货运经济效益和时效性低下的问题,开展航空集装器（ULD）装箱算法研究。方法 应用先进的贪心算法与遗传算法相结合的启发式算法研究单个航空集装器的装箱最优问题。结果 对单个航空集装器（ULD）装箱进行了装箱模型构建和算法优化,使得节省的航空集装器空间得到全部利用,实现最优装箱。结论 文中算法计算出的装载方案较人工计算更精确、更具稳定性,且经济效益更高。后续还有望把这种算法转化为高度智能化的软件系统,对航空货运自动化和工作流程标准化具有一定的推动意义。